1_Markdown

Markdown

library(shiny)
library(ggplot2)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1
## v purrr   0.3.4
## Warning: package 'tibble' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

What is rmarkdown?

  • .Rmd files · Develop your code and ideas side-by-side in a single document. Run code as

    individual chunks or as an entire document.

  • Dynamic Documents · Knit together plots, tables, and results with narrative text. Render to a variety of

formats like HTML, PDF, MS Word,Dashboard or MS Powerpoint

  • Reproducible Research · Upload, link to, or attach your report to share.Anyone can read

    or run your code to reproduce your work.

Font-Family: Verdana, Cursive 
Colors Used: ['crimson', 'orange', #202EF9, #FE1C0A, #159364] 
Emoji Used: [📚, 📌, 😃, 💬, 📎, 🏆, 🔭, 🌈, 💭, ⏳, 🙌 ✔️   ☑️]  

This notebook is created for people who want to make their notebooks look āesthetic and more pleasing to the viewer. We are not going to solve any machine learning problem in this notebook (you can check my other notebooks for ML and EDA). We will only focus on the HTML & Markdown to make your notebooks looks prettier and beutiful 🙌🏻 .


📌   Please keep in mind that, I am a beginner and on my learning journey as most of you. I just want to share something that will be useful for many kagglers working hard on their notebooks. If you find this notebook useful in anyway, please upvote it so that it can reach a bigger audience. You can share it with your fellow kagglers.

Workflow

  1. Open a new .Rmd file in the RStudio IDE by going to File > New File > R Markdown.

  2. Embed code in chunks. Run code by line, by chunk, or all at once.

  3. Write text and add tables, figures, images, and citations. Format with

    Markdown syntax or the RStudio Visual Markdown Editor.

  4. Set output format(s) and options in the YAML header. Customize themes or

add parameters to execute or add interactivity with Shiny.
  1. Save and render the whole document. Knit periodically to preview your work as you write.

  2. Share your work!

Take tour in rmd :

✔️ new file

✔️ CODE CHUNKS

✔️ SET GLOBAL OPTIONS

Set options for the entire document in the first chunk.

✔️ INLINE CODE

Insert 53940 into text sections. Code is evaluated

at render and results appear as text. “Built with r nrow(diamonds)”

✔️ save and render

✔️ see visual editor

✔️ see outline

OPTION DEFAULT EFFECT
echo TRUE display code in output document
error FALSE

TRUE (display error messages in doc)

FALSE (stop render when error occurs)

eval TRUE run code in chunk
include TRUE include chunk in doc after running
message TRUE display code messages in document
warning TRUE display code warnings in document
results “markup”

“asis” (passthrough results)

“hide” (don’t display results)

“hold” (put all results below all code)

fig.align “default” “left”, “right”, or “center”
fig.alt NULL alt text for a figure
fig.cap NULL figure caption as a character string
fig.path “figure/” prefix for generating figure file paths
fig.width & fig.height 7 plot dimensions in inches
out.width rescales output width, e.g. “75%”, “300px”
collapse FALSE collapse all sources & output into a single block
comment “##” include or exclude a code chunk when prefix for each line of results
child NULL files(s) to knit and then include
purl TRUE extracting source code with knitr::purl()

Insert Tables

Output data frames as tables using kable(data, caption).

data <- faithful[1:4, ]
knitr::kable(data,
caption = "Table with kable")
Table with kable
eruptions waiting
3.600 79
1.800 54
3.333 74
2.283 62

Write with Markdown

✔️ Plain text.

✔️ End a line with two spaces to start a new paragraph.

✔️ Also end with a backslash\ to make a new line.

✔️ italisc and bold

✔️ superscript^2^/subscript~2~

✔️ ~strikethrough

✔️ escaped: * _ \

✔️ endash: –, emdash: —

✔️ # Header 1

✔️ ## Header 2

✔️ ###### Header 6

✔️ - unordered list

  • item 2

  • item 2a (indent 1 tab)

  • item 2b

✔️ 1. ordered list

  1. item 2
  • item 2a (indent 1 tab)

  • item 2b

✔️ This is a link.

✔️ [This is another link][id].

✔️ verbatim code

✔️ multiple lines

 of verbatim code

✔️ > block quotes

✔️equation: $e^{i /pi} + 1 = 0$

✔️equation block:

✔️ $$E = mc^{2}$$

HTML Tabsets

Results

Plots text text

Tables more text

output

OUTPUT FORMAT CREATES
html_document .html
pdf_document * .pdf
word_document Microsoft Word (.docx)
powerpoint_presentation Microsoft Powerpoint (.pptx)
odt_document OpenDocument Text
rtf_document Rich Text Format .rtf
md_document Markdown .md
github_document Markdown for Github
ioslides_presentation ioslides HTML slides
slidy_presentation Slidy HTML slides
beamer_presentation * Beamer slides

Requires LaTeX, use tinytex::install_tinytex()

Also see flexdashboard, bookdown, distill, and blogdown.

+==========================================================+ +———————————————————-+

params

  1. Add parameters in the header as sub-values of params.
  2. Call parameters in code using params$<name>.
  3. Set parameters with Knit with Parameters or the params argument of render().

REUSABLE TEMPLATES

1. Create a new package with a inst/rmarkdown/

templates directory.

2. Add a folder containing template.yaml (below)

and skeleton.Rmd (template contents).


name: “My Template”


3. Install the package to access template by going to

File > New R Markdown > From Template.

Col1 Col2 html pdf word pptx
anchor_sections Show section anchors on mouse hover (TRUE or FALSE) X
citation_package The LaTeX package to process citations (“default”, “natbib”, “biblatex”) X X
code_download Give readers an option to download the .Rmd source code (TRUE or FALSE) X X
code_folding Let readers to toggle the display of R code (“none”, “hide”, or “show”) X
css CSS or SCSS file to use to style document (e.g. “style.css”) X X
dev Graphics device to use for figure output (e.g. “png”, “pdf”) X X
df_print Method for printing data frames (“default”, “kable”, “tibble”, “paged”) X X X X
fig_caption Should figures be rendered with captions (TRUE or FALSE) X X X X
highlight Syntax highlighting (“tango”, “pygments”, “kate”, “zenburn”, “textmate”) X X X
includes File of content to place in doc (“in_header”, “before_body”, “after_body”) X X
keep_md Keep the Markdown .md file generated by knitting (TRUE or FALSE) X X X X
keep_tex Keep the intermediate TEX file used to convert to PDF (TRUE or FALSE) X
latex_engine LaTeX engine for producing PDF output (“pdflatex”, “xelatex”, or “lualatex”) X
reference_docx/_doc docx/pptx file containing styles to copy in the output (e.g. “file.docx”, “file.pptx”) X X
theme Theme options (see Bootswatch and Custom Themes below) X
toc Add a table of contents at start of document (TRUE or FALSE) X X X X
toc_depth The lowest level of headings to add to table of contents (e.g. 2, 3) X X X X
toc_float Float the table of contents to the left of the main document content (TRUE or FALSE) X

BOOTSWATCH THEMES

Customize HTML documents with Bootswatch themes from the bslib package using the theme

output option.Use bslib::bootswatch_themes() to list available themes.


output:

html_document:

theme:

bootswatch: solar


CUSTOM THEMES

Customize individual HTML elements using bslib

variables. Use ?bs_theme to see more variables.


output:

html_document:

theme:

bg: “#121212”

fg: “#E4E4E4”

base_font:

google: “Prompt”


More on bslib at pkgs.rstudio.com/bslib/.

STYLING WITH CSS AND SCSS

Add CSS and SCSS to your document by adding a

path to a file with the css option in the YAML header.

title: “My Document”

author: “Author Name”

output:

html_document:

css: “style.css”


Apply CSS styling by writing HTML tags directly or:

• Use markdown to apply style attributes inline.

Bracketed Span

A green word.

Fenced Div

All of these words

are green.

Render

When you render a document, rmarkdown:

1. Runs the code and embeds results and text into an .md file with knitr.

2. Converts the .md file into the output format with Pandoc.

Save, then Knit to preview the document output.

The resulting HTML/PDF/MS Word/etc. document will be created and saved in the same directory as

the .Rmd file.Use rmarkdown::render() to render/knit in the R

console. See ?render for available options.

Share

Publish on RStudio Connectto share R Markdown documents

securely, schedule automaticupdates, and interact with parameters in real time.

rstudio.com/products/connect/

INTERACTIVITY

1. Add runtime: shiny to the YAML header.

2. Call Shiny input functions to embed input objects.

3. Call Shiny render functions to embed reactiveoutput.

4. Render with rmarkdown::run() or click Run Document in RStudio IDE.

Also see Shiny Prerendered for better performance.

rmarkdown.rstudio.com/

authoring_shiny_prerendered

Embed a complete app into your document with

shiny::shinyAppDir(). More at bookdown.org/yihui/

rmarkdown/shiny-embedded.html.

Bookdown

Maybe you want to write a technical book, or maybe your paper/write-up is so big that you need to split it into chapters. bookdown is an R package which allows you to construct a book structure to your output. You can write your chapters in separate R Markdown files headed with # level headings. You can employ an easy reference format to reference a bibliography or other other sections, chapters, figures or tables. You can then render the entire book in some neat HTML formats like Gitbook or Bootstrap, or you can render it as a pdf or epub format. Here’s an example of a recent book I wrote in Gitbook and in Bootstrap 4 (development version of bookdown). More on bookdown here.

#library(bookdown)
#library(downlit)
#bookdown::bs4_book()
#browseURL("https://bookdown.org/yihui/rmarkdown-cookbook/notebook.html")

interactive graphics

Use interactive graphics in your documents by embedding `plotly`

It’s a really effective teaching tool to allow your readers to interact

with your data or graphics as part of your R markdown documents.

Personally I love `plotly` for generating interactive graphics in 2D and

3D. You can insert plotly code into a code chunk in an R Markdown

document (it can be coded in R or Python — see Point 3), and this will

generate a beautiful graphic that the reader can interact with to see

data points, rotate, or whatever. Here’s an example. More on `plotly`

here.](https://plotly.com/r/).)

library(plotly)

library(gapminder)

p <- gapminder %>%
  
    filter(year==1977) %>%
  
    ggplot( aes(gdpPercap, lifeExp, size = pop, color=continent)) +
  
    geom_point() +
  
    scale_x_log10() +
  
    theme_bw()



ggplotly(p)
library(DT)

datatable(mtcars, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
  • This is my first conclusion

  • This is my second conclusion

Presentations

Presentations work by dividing your content into slides, with a new slide beginning at each first (#) or second (##) level header. You can also insert a horizontal rule (***) to create a new slide without a header. R Markdown comes with three presentation formats built-in:

  1. ioslides_presentation - HTML presentation with ioslides

  2. slidy_presentation - HTML presentation with W3C Slidy

  3. beamer_presentation - PDF presentation with LaTeX Beamer.

    Dashboards

    Dashboards are a useful way to communicate large amounts of information visually and quickly. Flexdashboard makes it particularly easy to create dashboards using R Markdown and a convention for how the headers affect the layout:

    • Each level 1 header (#) begins a new page in the dashboard.

    • Each level 2 header (##) begins a new column.

    • Each level 3 header (###) begins a new row.

# ---
# title: "Diamonds distribution dashboard"
# output: flexdashboard::flex_dashboard
# ---
# 
# library(ggplot2)
# library(dplyr)
# knitr::opts_chunk$set(fig.width = 5, fig.asp = 1/3)
# 
#  ## Column 1
# ### Carat
#  ggplot(diamonds, aes(carat)) + geom_histogram(binwidth = 0.1)
# 
# ### Cut
# ggplot(diamonds, aes(cut)) + geom_bar()
# ### Colour
# ggplot(diamonds, aes(color)) + geom_bar()
# ## Column 2
# ### The largest diamonds
#             diamonds %>% 
#               arrange(desc(carat)) %>% 
#               head(100) %>% 
#               select(carat, cut, color, price) %>% 
#               DT::datatable()

Shiny

theme_set(theme_minimal())

ui <- fluidPage(
  #Create input for domain (single) and variable (multiple)
  selectInput("domain", "Domain", choices = ""),
  selectInput("varSelection", "Choose 2 or 3 variables", multiple = T, choices = ""),
  
  #Set plot for output
  plotOutput("myPlot")
)

server <- function(input, output, session) {
  
  #Load your data
  df <- tibble(d = c(1,1,2,2),
               year = c(2015, 2016, 2015, 2016),
               v1 = c(3,5,4,10),
               v2 = c(7,11,13,18),
               v3 = c(1,2,3,4))
  
  #Update the domain input according to the data
  updateSelectInput(session, "domain", choices = sort(unique(df$d)))
  
  #Update the variable list (assumed all but d and year are variables of interest)
  updateSelectInput(session, "varSelection", 
                    choices = colnames(df %>% select(-d, -year)))
  
  #Load the chart function
  draw_chart <- function(df, listv, d){
    df2 <- df %>%
      gather("variable", "value", 3:5)
    df3 <- df2 %>%
      filter(variable %in% listv)
    
    df4 <- df3 %>%
      group_by(d, year) %>%
      summarise(value = mean(value)) %>%
      mutate(variable = "m")
    
    df5 <- bind_rows(df3, df4) 
    
    df5 <- df5 %>%
      mutate(year = as.character(year)) %>%
      mutate(year = as.Date(year, "%Y"))
    
    df5 <- df5 %>%
      mutate(year = lubridate::year(year))
    
    df5 <- df5 %>%
      filter(d == 1)
    # format(df5$year, "%Y")
    # Visualization
    ggplot(df5, aes(x = year, y = value)) + 
      geom_line(aes(color = variable, linetype = variable)) + 
      scale_color_discrete() +
      scale_x_continuous(breaks = c(2015,2016))
  }
  
  #Render the plot
  output$myPlot = renderPlot({
    #Only render if there are 2 or 3 variables selected
    req(between(length(input$varSelection), 2, 3))
    draw_chart(df, input$varSelection, input$domain)
  })
  
}

shinyApp(ui, server)